Overview

Dataset statistics

Number of variables22
Number of observations550
Missing cells1078
Missing cells (%)8.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory94.7 KiB
Average record size in memory176.2 B

Variable types

Categorical10
Numeric11
Unsupported1

Alerts

congress has constant value ""Constant
state_abbrev has a high cardinality: 51 distinct valuesHigh cardinality
bioname has a high cardinality: 549 distinct valuesHigh cardinality
bioguide_id has a high cardinality: 549 distinct valuesHigh cardinality
nominate_number_of_votes has a high cardinality: 171 distinct valuesHigh cardinality
nominate_number_of_errors has a high cardinality: 139 distinct valuesHigh cardinality
icpsr is highly overall correlated with chamberHigh correlation
state_icpsr is highly overall correlated with chamber and 2 other fieldsHigh correlation
died is highly overall correlated with occupancy and 1 other fieldsHigh correlation
nominate_dim1 is highly overall correlated with nokken_poole_dim1 and 1 other fieldsHigh correlation
nominate_dim2 is highly overall correlated with nokken_poole_dim2High correlation
nominate_log_likelihood is highly overall correlated with nominate_geo_mean_probabilityHigh correlation
nominate_geo_mean_probability is highly overall correlated with nominate_log_likelihoodHigh correlation
nokken_poole_dim1 is highly overall correlated with nominate_dim1 and 1 other fieldsHigh correlation
nokken_poole_dim2 is highly overall correlated with nominate_dim2High correlation
chamber is highly overall correlated with icpsr and 3 other fieldsHigh correlation
state_abbrev is highly overall correlated with state_icpsr and 2 other fieldsHigh correlation
party_code is highly overall correlated with nominate_dim1 and 1 other fieldsHigh correlation
occupancy is highly overall correlated with died and 1 other fieldsHigh correlation
last_means is highly overall correlated with state_icpsr and 4 other fieldsHigh correlation
chamber is highly imbalanced (54.4%)Imbalance
occupancy is highly imbalanced (82.7%)Imbalance
last_means is highly imbalanced (87.7%)Imbalance
died has 522 (94.9%) missing valuesMissing
conditional has 550 (100.0%) missing valuesMissing
bioname is uniformly distributedUniform
bioguide_id is uniformly distributedUniform
conditional is an unsupported type, check if it needs cleaning or further analysisUnsupported
district_code has 106 (19.3%) zerosZeros

Reproduction

Analysis started2023-08-28 21:37:23.204867
Analysis finished2023-08-28 21:37:36.373533
Duration13.17 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

congress
Categorical

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
113
550 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1650
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row113
2nd row113
3rd row113
4th row113
5th row113

Common Values

ValueCountFrequency (%)
113 550
100.0%

Length

2023-08-28T14:37:36.418841image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-28T14:37:36.515865image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
113 550
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1100
66.7%
3 550
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1650
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1100
66.7%
3 550
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1650
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1100
66.7%
3 550
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1650
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1100
66.7%
3 550
33.3%

chamber
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
House
444 
Senate
105 
President
 
1

Length

Max length9
Median length5
Mean length5.1981818
Min length5

Characters and Unicode

Total characters2859
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st rowPresident
2nd rowHouse
3rd rowHouse
4th rowHouse
5th rowHouse

Common Values

ValueCountFrequency (%)
House 444
80.7%
Senate 105
 
19.1%
President 1
 
0.2%

Length

2023-08-28T14:37:36.589387image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-28T14:37:36.694908image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
house 444
80.7%
senate 105
 
19.1%
president 1
 
0.2%

Most occurring characters

ValueCountFrequency (%)
e 656
22.9%
s 445
15.6%
H 444
15.5%
o 444
15.5%
u 444
15.5%
n 106
 
3.7%
t 106
 
3.7%
S 105
 
3.7%
a 105
 
3.7%
P 1
 
< 0.1%
Other values (3) 3
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2309
80.8%
Uppercase Letter 550
 
19.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 656
28.4%
s 445
19.3%
o 444
19.2%
u 444
19.2%
n 106
 
4.6%
t 106
 
4.6%
a 105
 
4.5%
r 1
 
< 0.1%
i 1
 
< 0.1%
d 1
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
H 444
80.7%
S 105
 
19.1%
P 1
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 2859
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 656
22.9%
s 445
15.6%
H 444
15.5%
o 444
15.5%
u 444
15.5%
n 106
 
3.7%
t 106
 
3.7%
S 105
 
3.7%
a 105
 
3.7%
P 1
 
< 0.1%
Other values (3) 3
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2859
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 656
22.9%
s 445
15.6%
H 444
15.5%
o 444
15.5%
u 444
15.5%
n 106
 
3.7%
t 106
 
3.7%
S 105
 
3.7%
a 105
 
3.7%
P 1
 
< 0.1%
Other values (3) 3
 
0.1%

icpsr
Real number (ℝ)

Distinct549
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24424.635
Minimum2605
Maximum99911
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2023-08-28T14:37:36.785886image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2605
5-th percentile14869.9
Q120531.25
median21167.5
Q329347.5
95-th percentile41017.75
Maximum99911
Range97306
Interquartile range (IQR)8816.25

Descriptive statistics

Standard deviation9464.683
Coefficient of variation (CV)0.38750562
Kurtosis21.498919
Mean24424.635
Median Absolute Deviation (MAD)823
Skewness3.5405803
Sum13433549
Variance89580225
MonotonicityNot monotonic
2023-08-28T14:37:36.898873image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14435 2
 
0.4%
99911 1
 
0.2%
21180 1
 
0.2%
20351 1
 
0.2%
20748 1
 
0.2%
20947 1
 
0.2%
21178 1
 
0.2%
21179 1
 
0.2%
21181 1
 
0.2%
15019 1
 
0.2%
Other values (539) 539
98.0%
ValueCountFrequency (%)
2605 1
0.2%
10713 1
0.2%
13035 1
0.2%
13047 1
0.2%
14009 1
0.2%
14066 1
0.2%
14203 1
0.2%
14226 1
0.2%
14230 1
0.2%
14256 1
0.2%
ValueCountFrequency (%)
99911 1
0.2%
94828 1
0.2%
94659 1
0.2%
90327 1
0.2%
49706 1
0.2%
49703 1
0.2%
49702 1
0.2%
49700 1
0.2%
49308 1
0.2%
49300 1
0.2%

state_icpsr
Real number (ℝ)

Distinct51
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.672727
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2023-08-28T14:37:37.013552image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q123
median43
Q354
95-th percentile71
Maximum99
Range98
Interquartile range (IQR)31

Descriptive statistics

Standard deviation21.435509
Coefficient of variation (CV)0.52702413
Kurtosis-0.96680088
Mean40.672727
Median Absolute Deviation (MAD)19
Skewness-0.053536374
Sum22370
Variance459.48104
MonotonicityNot monotonic
2023-08-28T14:37:37.125060image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
71 55
 
10.0%
49 38
 
6.9%
43 31
 
5.6%
13 29
 
5.3%
14 20
 
3.6%
21 20
 
3.6%
24 18
 
3.3%
12 17
 
3.1%
23 16
 
2.9%
44 16
 
2.9%
Other values (41) 290
52.7%
ValueCountFrequency (%)
1 7
 
1.3%
2 4
 
0.7%
3 14
2.5%
4 4
 
0.7%
5 4
 
0.7%
6 3
 
0.5%
11 3
 
0.5%
12 17
3.1%
13 29
5.3%
14 20
3.6%
ValueCountFrequency (%)
99 1
 
0.2%
82 4
 
0.7%
81 3
 
0.5%
73 12
 
2.2%
72 7
 
1.3%
71 55
10.0%
68 3
 
0.5%
67 6
 
1.1%
66 5
 
0.9%
65 6
 
1.1%

district_code
Real number (ℝ)

Distinct54
Distinct (%)9.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.1763636
Minimum0
Maximum53
Zeros106
Zeros (%)19.3%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2023-08-28T14:37:37.233744image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median5
Q311
95-th percentile31
Maximum53
Range53
Interquartile range (IQR)10

Descriptive statistics

Standard deviation10.301104
Coefficient of variation (CV)1.2598637
Kurtosis4.1376228
Mean8.1763636
Median Absolute Deviation (MAD)4
Skewness2.0037893
Sum4497
Variance106.11274
MonotonicityNot monotonic
2023-08-28T14:37:37.348328image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 106
19.3%
1 52
 
9.5%
2 43
 
7.8%
3 38
 
6.9%
4 35
 
6.4%
5 31
 
5.6%
6 26
 
4.7%
7 25
 
4.5%
8 22
 
4.0%
9 17
 
3.1%
Other values (44) 155
28.2%
ValueCountFrequency (%)
0 106
19.3%
1 52
9.5%
2 43
7.8%
3 38
 
6.9%
4 35
 
6.4%
5 31
 
5.6%
6 26
 
4.7%
7 25
 
4.5%
8 22
 
4.0%
9 17
 
3.1%
ValueCountFrequency (%)
53 1
0.2%
52 1
0.2%
51 1
0.2%
50 1
0.2%
49 1
0.2%
48 1
0.2%
47 1
0.2%
46 1
0.2%
45 1
0.2%
44 1
0.2%

state_abbrev
Categorical

HIGH CARDINALITY  HIGH CORRELATION 

Distinct51
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
CA
55 
TX
38 
FL
 
31
NY
 
29
PA
 
20
Other values (46)
377 

Length

Max length3
Median length2
Mean length2.0018182
Min length2

Characters and Unicode

Total characters1101
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st rowUSA
2nd rowAL
3rd rowAL
4th rowAL
5th rowAL

Common Values

ValueCountFrequency (%)
CA 55
 
10.0%
TX 38
 
6.9%
FL 31
 
5.6%
NY 29
 
5.3%
PA 20
 
3.6%
IL 20
 
3.6%
OH 18
 
3.3%
NJ 17
 
3.1%
MI 16
 
2.9%
GA 16
 
2.9%
Other values (41) 290
52.7%

Length

2023-08-28T14:37:37.446947image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ca 55
 
10.0%
tx 38
 
6.9%
fl 31
 
5.6%
ny 29
 
5.3%
pa 20
 
3.6%
il 20
 
3.6%
oh 18
 
3.3%
nj 17
 
3.1%
mi 16
 
2.9%
ga 16
 
2.9%
Other values (41) 290
52.7%

Most occurring characters

ValueCountFrequency (%)
A 177
16.1%
N 117
10.6%
C 96
 
8.7%
M 80
 
7.3%
I 75
 
6.8%
L 70
 
6.4%
T 69
 
6.3%
O 52
 
4.7%
Y 40
 
3.6%
X 38
 
3.5%
Other values (14) 287
26.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1101
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 177
16.1%
N 117
10.6%
C 96
 
8.7%
M 80
 
7.3%
I 75
 
6.8%
L 70
 
6.4%
T 69
 
6.3%
O 52
 
4.7%
Y 40
 
3.6%
X 38
 
3.5%
Other values (14) 287
26.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 1101
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 177
16.1%
N 117
10.6%
C 96
 
8.7%
M 80
 
7.3%
I 75
 
6.8%
L 70
 
6.4%
T 69
 
6.3%
O 52
 
4.7%
Y 40
 
3.6%
X 38
 
3.5%
Other values (14) 287
26.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1101
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 177
16.1%
N 117
10.6%
C 96
 
8.7%
M 80
 
7.3%
I 75
 
6.8%
L 70
 
6.4%
T 69
 
6.3%
O 52
 
4.7%
Y 40
 
3.6%
X 38
 
3.5%
Other values (14) 287
26.1%

party_code
Categorical

Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
200
286 
100
262 
328
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1650
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row100
2nd row200
3rd row200
4th row100
5th row200

Common Values

ValueCountFrequency (%)
200 286
52.0%
100 262
47.6%
328 2
 
0.4%

Length

2023-08-28T14:37:37.528980image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-28T14:37:37.614852image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
200 286
52.0%
100 262
47.6%
328 2
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 1096
66.4%
2 288
 
17.5%
1 262
 
15.9%
3 2
 
0.1%
8 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1650
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1096
66.4%
2 288
 
17.5%
1 262
 
15.9%
3 2
 
0.1%
8 2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1650
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1096
66.4%
2 288
 
17.5%
1 262
 
15.9%
3 2
 
0.1%
8 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1650
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1096
66.4%
2 288
 
17.5%
1 262
 
15.9%
3 2
 
0.1%
8 2
 
0.1%

occupancy
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
0
523 
1
 
13
2
 
12
3
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters550
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 523
95.1%
1 13
 
2.4%
2 12
 
2.2%
3 2
 
0.4%

Length

2023-08-28T14:37:37.689660image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-28T14:37:37.781247image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 523
95.1%
1 13
 
2.4%
2 12
 
2.2%
3 2
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 523
95.1%
1 13
 
2.4%
2 12
 
2.2%
3 2
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 550
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 523
95.1%
1 13
 
2.4%
2 12
 
2.2%
3 2
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 550
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 523
95.1%
1 13
 
2.4%
2 12
 
2.2%
3 2
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 550
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 523
95.1%
1 13
 
2.4%
2 12
 
2.2%
3 2
 
0.4%

last_means
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
1
532 
2
 
12
5
 
5
0
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters550
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 532
96.7%
2 12
 
2.2%
5 5
 
0.9%
0 1
 
0.2%

Length

2023-08-28T14:37:37.862902image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-28T14:37:37.949642image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1 532
96.7%
2 12
 
2.2%
5 5
 
0.9%
0 1
 
0.2%

Most occurring characters

ValueCountFrequency (%)
1 532
96.7%
2 12
 
2.2%
5 5
 
0.9%
0 1
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 550
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 532
96.7%
2 12
 
2.2%
5 5
 
0.9%
0 1
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 550
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 532
96.7%
2 12
 
2.2%
5 5
 
0.9%
0 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 550
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 532
96.7%
2 12
 
2.2%
5 5
 
0.9%
0 1
 
0.2%

bioname
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct549
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
MARKEY, Edward John
 
2
OBAMA, Barack
 
1
BLACK, Diane
 
1
BLACKBURN, Marsha
 
1
COHEN, Stephen
 
1
Other values (544)
544 

Length

Max length42
Median length31
Mean length16.505455
Min length8

Characters and Unicode

Total characters9078
Distinct characters63
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique548 ?
Unique (%)99.6%

Sample

1st rowOBAMA, Barack
2nd rowBONNER, Jr., Josiah Robins (Jo)
3rd rowROGERS, Mike Dennis
4th rowSEWELL, Terri
5th rowROBY, Martha

Common Values

ValueCountFrequency (%)
MARKEY, Edward John 2
 
0.4%
OBAMA, Barack 1
 
0.2%
BLACK, Diane 1
 
0.2%
BLACKBURN, Marsha 1
 
0.2%
COHEN, Stephen 1
 
0.2%
ROE, David P. (Phil) 1
 
0.2%
FLEISCHMANN, Chuck 1
 
0.2%
DESJARLAIS, Scott 1
 
0.2%
FINCHER, Stephen Lee 1
 
0.2%
COOPER, James Hayes Shofner 1
 
0.2%
Other values (539) 539
98.0%

Length

2023-08-28T14:37:38.053767image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
john 32
 
2.3%
jr 16
 
1.1%
michael 16
 
1.1%
david 15
 
1.1%
robert 14
 
1.0%
james 13
 
0.9%
richard 13
 
0.9%
a 12
 
0.9%
scott 11
 
0.8%
william 11
 
0.8%
Other values (856) 1241
89.0%

Most occurring characters

ValueCountFrequency (%)
844
 
9.3%
, 572
 
6.3%
E 415
 
4.6%
R 403
 
4.4%
e 375
 
4.1%
A 358
 
3.9%
a 354
 
3.9%
N 303
 
3.3%
S 295
 
3.2%
n 267
 
2.9%
Other values (53) 4892
53.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 4461
49.1%
Lowercase Letter 2979
32.8%
Space Separator 844
 
9.3%
Other Punctuation 700
 
7.7%
Open Punctuation 45
 
0.5%
Close Punctuation 45
 
0.5%
Dash Punctuation 4
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 415
 
9.3%
R 403
 
9.0%
A 358
 
8.0%
N 303
 
6.8%
S 295
 
6.6%
L 261
 
5.9%
O 253
 
5.7%
T 224
 
5.0%
I 218
 
4.9%
M 205
 
4.6%
Other values (18) 1526
34.2%
Lowercase Letter
ValueCountFrequency (%)
e 375
12.6%
a 354
11.9%
n 267
 
9.0%
r 258
 
8.7%
i 252
 
8.5%
o 220
 
7.4%
l 177
 
5.9%
h 149
 
5.0%
t 132
 
4.4%
d 119
 
4.0%
Other values (18) 676
22.7%
Other Punctuation
ValueCountFrequency (%)
, 572
81.7%
. 127
 
18.1%
' 1
 
0.1%
Space Separator
ValueCountFrequency (%)
844
100.0%
Open Punctuation
ValueCountFrequency (%)
( 45
100.0%
Close Punctuation
ValueCountFrequency (%)
) 45
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 7440
82.0%
Common 1638
 
18.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 415
 
5.6%
R 403
 
5.4%
e 375
 
5.0%
A 358
 
4.8%
a 354
 
4.8%
N 303
 
4.1%
S 295
 
4.0%
n 267
 
3.6%
L 261
 
3.5%
r 258
 
3.5%
Other values (46) 4151
55.8%
Common
ValueCountFrequency (%)
844
51.5%
, 572
34.9%
. 127
 
7.8%
( 45
 
2.7%
) 45
 
2.7%
- 4
 
0.2%
' 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9068
99.9%
None 10
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
844
 
9.3%
, 572
 
6.3%
E 415
 
4.6%
R 403
 
4.4%
e 375
 
4.1%
A 358
 
3.9%
a 354
 
3.9%
N 303
 
3.3%
S 295
 
3.3%
n 267
 
2.9%
Other values (49) 4882
53.8%
None
ValueCountFrequency (%)
Á 4
40.0%
é 3
30.0%
ú 2
20.0%
É 1
 
10.0%

bioguide_id
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct549
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
M000133
 
2
O000167
 
1
B001273
 
1
B001243
 
1
C001068
 
1
Other values (544)
544 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters3850
Distinct characters34
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique548 ?
Unique (%)99.6%

Sample

1st rowO000167
2nd rowB001244
3rd rowR000575
4th rowS001185
5th rowR000591

Common Values

ValueCountFrequency (%)
M000133 2
 
0.4%
O000167 1
 
0.2%
B001273 1
 
0.2%
B001243 1
 
0.2%
C001068 1
 
0.2%
R000582 1
 
0.2%
F000459 1
 
0.2%
D000616 1
 
0.2%
F000458 1
 
0.2%
C000754 1
 
0.2%
Other values (539) 539
98.0%

Length

2023-08-28T14:37:38.148994image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
m000133 2
 
0.4%
y000033 1
 
0.2%
b001279 1
 
0.2%
s001183 1
 
0.2%
g000565 1
 
0.2%
k000368 1
 
0.2%
g000551 1
 
0.2%
f000448 1
 
0.2%
a000055 1
 
0.2%
l000397 1
 
0.2%
Other values (539) 539
98.0%

Most occurring characters

ValueCountFrequency (%)
0 1676
43.5%
1 383
 
9.9%
5 226
 
5.9%
6 166
 
4.3%
2 154
 
4.0%
8 154
 
4.0%
7 141
 
3.7%
3 136
 
3.5%
4 133
 
3.5%
9 131
 
3.4%
Other values (24) 550
 
14.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3300
85.7%
Uppercase Letter 550
 
14.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 62
11.3%
M 52
 
9.5%
B 52
 
9.5%
S 48
 
8.7%
R 36
 
6.5%
H 36
 
6.5%
L 31
 
5.6%
G 29
 
5.3%
W 28
 
5.1%
P 27
 
4.9%
Other values (14) 149
27.1%
Decimal Number
ValueCountFrequency (%)
0 1676
50.8%
1 383
 
11.6%
5 226
 
6.8%
6 166
 
5.0%
2 154
 
4.7%
8 154
 
4.7%
7 141
 
4.3%
3 136
 
4.1%
4 133
 
4.0%
9 131
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3300
85.7%
Latin 550
 
14.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 62
11.3%
M 52
 
9.5%
B 52
 
9.5%
S 48
 
8.7%
R 36
 
6.5%
H 36
 
6.5%
L 31
 
5.6%
G 29
 
5.3%
W 28
 
5.1%
P 27
 
4.9%
Other values (14) 149
27.1%
Common
ValueCountFrequency (%)
0 1676
50.8%
1 383
 
11.6%
5 226
 
6.8%
6 166
 
5.0%
2 154
 
4.7%
8 154
 
4.7%
7 141
 
4.3%
3 136
 
4.1%
4 133
 
4.0%
9 131
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3850
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1676
43.5%
1 383
 
9.9%
5 226
 
5.9%
6 166
 
4.3%
2 154
 
4.0%
8 154
 
4.0%
7 141
 
3.7%
3 136
 
3.5%
4 133
 
3.5%
9 131
 
3.4%
Other values (24) 550
 
14.3%

born
Real number (ℝ)

Distinct55
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1954.9764
Minimum1923
Maximum1983
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2023-08-28T14:37:38.254929image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1923
5-th percentile1937
Q11947
median1955
Q31962
95-th percentile1973.55
Maximum1983
Range60
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.893481
Coefficient of variation (CV)0.0055721804
Kurtosis-0.19505073
Mean1954.9764
Median Absolute Deviation (MAD)7.5
Skewness0.034973986
Sum1075237
Variance118.66793
MonotonicityNot monotonic
2023-08-28T14:37:38.360328image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1955 24
 
4.4%
1947 24
 
4.4%
1951 23
 
4.2%
1952 23
 
4.2%
1958 22
 
4.0%
1950 20
 
3.6%
1953 19
 
3.5%
1946 18
 
3.3%
1963 18
 
3.3%
1956 18
 
3.3%
Other values (45) 341
62.0%
ValueCountFrequency (%)
1923 1
 
0.2%
1924 1
 
0.2%
1926 1
 
0.2%
1929 2
 
0.4%
1930 3
0.5%
1931 2
 
0.4%
1933 3
0.5%
1934 4
0.7%
1935 1
 
0.2%
1936 6
1.1%
ValueCountFrequency (%)
1983 1
 
0.2%
1981 2
 
0.4%
1980 4
0.7%
1978 3
 
0.5%
1977 3
 
0.5%
1976 6
1.1%
1975 4
0.7%
1974 5
0.9%
1973 8
1.5%
1972 6
1.1%

died
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)25.0%
Missing522
Missing (%)94.9%
Infinite0
Infinite (%)0.0%
Mean2019.1071
Minimum2013
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2023-08-28T14:37:38.459933image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2013
5-th percentile2013.7
Q12018.75
median2019.5
Q32021
95-th percentile2022
Maximum2022
Range9
Interquartile range (IQR)2.25

Descriptive statistics

Standard deviation2.4546145
Coefficient of variation (CV)0.001215693
Kurtosis1.2823679
Mean2019.1071
Median Absolute Deviation (MAD)1.5
Skewness-1.2981012
Sum56535
Variance6.0251323
MonotonicityNot monotonic
2023-08-28T14:37:38.529665image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2019 7
 
1.3%
2021 6
 
1.1%
2020 5
 
0.9%
2022 3
 
0.5%
2018 3
 
0.5%
2013 2
 
0.4%
2015 2
 
0.4%
(Missing) 522
94.9%
ValueCountFrequency (%)
2013 2
 
0.4%
2015 2
 
0.4%
2018 3
0.5%
2019 7
1.3%
2020 5
0.9%
2021 6
1.1%
2022 3
0.5%
ValueCountFrequency (%)
2022 3
0.5%
2021 6
1.1%
2020 5
0.9%
2019 7
1.3%
2018 3
0.5%
2015 2
 
0.4%
2013 2
 
0.4%

nominate_dim1
Real number (ℝ)

Distinct436
Distinct (%)79.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.071569091
Minimum-0.753
Maximum0.913
Zeros0
Zeros (%)0.0%
Negative264
Negative (%)48.0%
Memory size4.4 KiB
2023-08-28T14:37:38.625075image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-0.753
5-th percentile-0.514
Q1-0.3695
median0.2215
Q30.4895
95-th percentile0.6824
Maximum0.913
Range1.666
Interquartile range (IQR)0.859

Descriptive statistics

Standard deviation0.44775176
Coefficient of variation (CV)6.2562169
Kurtosis-1.6313485
Mean0.071569091
Median Absolute Deviation (MAD)0.433
Skewness-0.0010566082
Sum39.363
Variance0.20048164
MonotonicityNot monotonic
2023-08-28T14:37:38.729146image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.402 5
 
0.9%
-0.465 3
 
0.5%
0.527 3
 
0.5%
-0.469 3
 
0.5%
-0.367 3
 
0.5%
-0.389 3
 
0.5%
0.444 3
 
0.5%
-0.239 3
 
0.5%
0.425 3
 
0.5%
-0.343 3
 
0.5%
Other values (426) 518
94.2%
ValueCountFrequency (%)
-0.753 1
0.2%
-0.677 1
0.2%
-0.666 1
0.2%
-0.658 1
0.2%
-0.656 1
0.2%
-0.61 1
0.2%
-0.603 1
0.2%
-0.598 1
0.2%
-0.589 1
0.2%
-0.584 1
0.2%
ValueCountFrequency (%)
0.913 1
0.2%
0.899 1
0.2%
0.891 2
0.4%
0.855 1
0.2%
0.829 1
0.2%
0.806 1
0.2%
0.782 1
0.2%
0.75 1
0.2%
0.749 1
0.2%
0.748 1
0.2%

nominate_dim2
Real number (ℝ)

Distinct420
Distinct (%)76.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.018190909
Minimum-0.757
Maximum0.712
Zeros1
Zeros (%)0.2%
Negative289
Negative (%)52.5%
Memory size4.4 KiB
2023-08-28T14:37:38.842733image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-0.757
5-th percentile-0.4642
Q1-0.2045
median-0.013
Q30.18075
95-th percentile0.4101
Maximum0.712
Range1.469
Interquartile range (IQR)0.38525

Descriptive statistics

Standard deviation0.27527079
Coefficient of variation (CV)-15.132327
Kurtosis-0.37785076
Mean-0.018190909
Median Absolute Deviation (MAD)0.193
Skewness-0.10412696
Sum-10.005
Variance0.075774005
MonotonicityNot monotonic
2023-08-28T14:37:39.103681image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.154 4
 
0.7%
0.014 4
 
0.7%
0.308 4
 
0.7%
0.04 4
 
0.7%
-0.002 4
 
0.7%
-0.03 3
 
0.5%
-0.071 3
 
0.5%
0.054 3
 
0.5%
0.129 3
 
0.5%
0.208 3
 
0.5%
Other values (410) 515
93.6%
ValueCountFrequency (%)
-0.757 1
0.2%
-0.735 2
0.4%
-0.728 1
0.2%
-0.692 1
0.2%
-0.647 2
0.4%
-0.626 1
0.2%
-0.612 1
0.2%
-0.592 1
0.2%
-0.58 1
0.2%
-0.579 2
0.4%
ValueCountFrequency (%)
0.712 1
0.2%
0.658 1
0.2%
0.623 1
0.2%
0.582 1
0.2%
0.574 1
0.2%
0.57 1
0.2%
0.563 1
0.2%
0.561 1
0.2%
0.56 1
0.2%
0.546 1
0.2%

nominate_log_likelihood
Real number (ℝ)

Distinct548
Distinct (%)100.0%
Missing2
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean-136.3484
Minimum-939.25775
Maximum-0.35377
Zeros0
Zeros (%)0.0%
Negative548
Negative (%)99.6%
Memory size4.4 KiB
2023-08-28T14:37:39.225954image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-939.25775
5-th percentile-266.7117
Q1-160.15425
median-124.90126
Q3-99.772115
95-th percentile-23.221058
Maximum-0.35377
Range938.90398
Interquartile range (IQR)60.382135

Descriptive statistics

Standard deviation80.999222
Coefficient of variation (CV)-0.59406069
Kurtosis20.150606
Mean-136.3484
Median Absolute Deviation (MAD)29.222685
Skewness-2.8891749
Sum-74718.921
Variance6560.874
MonotonicityNot monotonic
2023-08-28T14:37:39.333728image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-41.04464 1
 
0.2%
-87.1063 1
 
0.2%
-362.81696 1
 
0.2%
-93.54589 1
 
0.2%
-133.9532 1
 
0.2%
-117.20282 1
 
0.2%
-104.13953 1
 
0.2%
-127.11761 1
 
0.2%
-118.39695 1
 
0.2%
-115.50238 1
 
0.2%
Other values (538) 538
97.8%
(Missing) 2
 
0.4%
ValueCountFrequency (%)
-939.25775 1
0.2%
-545.10044 1
0.2%
-524.56524 1
0.2%
-422.41261 1
0.2%
-418.94428 1
0.2%
-418.93384 1
0.2%
-397.61537 1
0.2%
-396.782 1
0.2%
-392.97464 1
0.2%
-389.42024 1
0.2%
ValueCountFrequency (%)
-0.35377 1
0.2%
-2.59462 1
0.2%
-3.69977 1
0.2%
-6.4221 1
0.2%
-7.6854 1
0.2%
-8.38874 1
0.2%
-8.89963 1
0.2%
-9.62895 1
0.2%
-10.69304 1
0.2%
-13.68922 1
0.2%
Distinct512
Distinct (%)93.4%
Missing2
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean0.8594542
Minimum0.36856
Maximum0.989
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2023-08-28T14:37:39.442713image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.36856
5-th percentile0.7487775
Q10.833885
median0.87107
Q30.8965325
95-th percentile0.948867
Maximum0.989
Range0.62044
Interquartile range (IQR)0.0626475

Descriptive statistics

Standard deviation0.063591692
Coefficient of variation (CV)0.073990787
Kurtosis7.9147234
Mean0.8594542
Median Absolute Deviation (MAD)0.029205
Skewness-1.7840302
Sum470.9809
Variance0.0040439033
MonotonicityNot monotonic
2023-08-28T14:37:39.546023image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.886 5
 
0.9%
0.848 4
 
0.7%
0.86 3
 
0.5%
0.855 3
 
0.5%
0.864 2
 
0.4%
0.89 2
 
0.4%
0.87583 2
 
0.4%
0.819 2
 
0.4%
0.836 2
 
0.4%
0.9 2
 
0.4%
Other values (502) 521
94.7%
ValueCountFrequency (%)
0.36856 1
0.2%
0.584 1
0.2%
0.596 1
0.2%
0.61522 1
0.2%
0.658 1
0.2%
0.65939 1
0.2%
0.66237 1
0.2%
0.66567 1
0.2%
0.671 1
0.2%
0.67352 1
0.2%
ValueCountFrequency (%)
0.989 1
0.2%
0.98126 1
0.2%
0.97638 1
0.2%
0.97478 1
0.2%
0.97309 1
0.2%
0.97224 1
0.2%
0.97128 1
0.2%
0.97057 1
0.2%
0.97 1
0.2%
0.96991 1
0.2%
Distinct171
Distinct (%)31.1%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
1012
 
17
1016
 
14
1018
 
14
998
 
13
1015
 
13
Other values (166)
479 

Length

Max length4
Median length3
Mean length3.3490909
Min length0

Characters and Unicode

Total characters1842
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique72 ?
Unique (%)13.1%

Sample

1st row327
2nd row354
3rd row1002
4th row1004
5th row1016

Common Values

ValueCountFrequency (%)
1012 17
 
3.1%
1016 14
 
2.5%
1018 14
 
2.5%
998 13
 
2.4%
1015 13
 
2.4%
999 11
 
2.0%
1019 11
 
2.0%
1017 10
 
1.8%
1009 10
 
1.8%
1006 10
 
1.8%
Other values (161) 427
77.6%

Length

2023-08-28T14:37:39.654298image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1012 17
 
3.1%
1018 14
 
2.6%
1016 14
 
2.6%
998 13
 
2.4%
1015 13
 
2.4%
999 11
 
2.0%
1019 11
 
2.0%
1017 10
 
1.8%
1009 10
 
1.8%
1006 10
 
1.8%
Other values (160) 425
77.6%

Most occurring characters

ValueCountFrequency (%)
1 383
20.8%
0 351
19.1%
9 330
17.9%
5 182
9.9%
8 121
 
6.6%
4 108
 
5.9%
6 100
 
5.4%
7 94
 
5.1%
2 92
 
5.0%
3 81
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1842
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 383
20.8%
0 351
19.1%
9 330
17.9%
5 182
9.9%
8 121
 
6.6%
4 108
 
5.9%
6 100
 
5.4%
7 94
 
5.1%
2 92
 
5.0%
3 81
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
Common 1842
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 383
20.8%
0 351
19.1%
9 330
17.9%
5 182
9.9%
8 121
 
6.6%
4 108
 
5.9%
6 100
 
5.4%
7 94
 
5.1%
2 92
 
5.0%
3 81
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1842
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 383
20.8%
0 351
19.1%
9 330
17.9%
5 182
9.9%
8 121
 
6.6%
4 108
 
5.9%
6 100
 
5.4%
7 94
 
5.1%
2 92
 
5.0%
3 81
 
4.4%
Distinct139
Distinct (%)25.3%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
45
 
18
61
 
14
57
 
13
52
 
12
47
 
11
Other values (134)
482 

Length

Max length3
Median length2
Mean length2.0436364
Min length0

Characters and Unicode

Total characters1124
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique49 ?
Unique (%)8.9%

Sample

1st row11
2nd row17
3rd row57
4th row80
5th row33

Common Values

ValueCountFrequency (%)
45 18
 
3.3%
61 14
 
2.5%
57 13
 
2.4%
52 12
 
2.2%
47 11
 
2.0%
64 11
 
2.0%
53 11
 
2.0%
38 11
 
2.0%
44 11
 
2.0%
55 9
 
1.6%
Other values (129) 429
78.0%

Length

2023-08-28T14:37:39.751210image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
45 18
 
3.3%
61 14
 
2.6%
57 13
 
2.4%
52 12
 
2.2%
47 11
 
2.0%
64 11
 
2.0%
53 11
 
2.0%
38 11
 
2.0%
44 11
 
2.0%
55 9
 
1.6%
Other values (128) 427
77.9%

Most occurring characters

ValueCountFrequency (%)
5 156
13.9%
4 145
12.9%
1 141
12.5%
6 129
11.5%
3 129
11.5%
7 108
9.6%
2 96
8.5%
8 85
7.6%
9 76
6.8%
0 59
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1124
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 156
13.9%
4 145
12.9%
1 141
12.5%
6 129
11.5%
3 129
11.5%
7 108
9.6%
2 96
8.5%
8 85
7.6%
9 76
6.8%
0 59
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
Common 1124
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 156
13.9%
4 145
12.9%
1 141
12.5%
6 129
11.5%
3 129
11.5%
7 108
9.6%
2 96
8.5%
8 85
7.6%
9 76
6.8%
0 59
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1124
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 156
13.9%
4 145
12.9%
1 141
12.5%
6 129
11.5%
3 129
11.5%
7 108
9.6%
2 96
8.5%
8 85
7.6%
9 76
6.8%
0 59
 
5.2%

conditional
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing550
Missing (%)100.0%
Memory size4.4 KiB

nokken_poole_dim1
Real number (ℝ)

Distinct428
Distinct (%)78.0%
Missing1
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean0.072087432
Minimum-0.944
Maximum0.991
Zeros0
Zeros (%)0.0%
Negative263
Negative (%)47.8%
Memory size4.4 KiB
2023-08-28T14:37:39.858161image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-0.944
5-th percentile-0.5232
Q1-0.388
median0.232
Q30.485
95-th percentile0.6986
Maximum0.991
Range1.935
Interquartile range (IQR)0.873

Descriptive statistics

Standard deviation0.45770271
Coefficient of variation (CV)6.349272
Kurtosis-1.5659495
Mean0.072087432
Median Absolute Deviation (MAD)0.435
Skewness0.01546934
Sum39.576
Variance0.20949177
MonotonicityNot monotonic
2023-08-28T14:37:39.970922image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.361 5
 
0.9%
0.467 4
 
0.7%
-0.419 4
 
0.7%
-0.413 4
 
0.7%
-0.439 3
 
0.5%
-0.435 3
 
0.5%
-0.271 3
 
0.5%
-0.254 3
 
0.5%
0.493 3
 
0.5%
0.549 3
 
0.5%
Other values (418) 514
93.5%
ValueCountFrequency (%)
-0.944 1
0.2%
-0.731 1
0.2%
-0.718 1
0.2%
-0.666 1
0.2%
-0.655 1
0.2%
-0.651 1
0.2%
-0.6 1
0.2%
-0.598 1
0.2%
-0.593 1
0.2%
-0.575 1
0.2%
ValueCountFrequency (%)
0.991 1
0.2%
0.986 1
0.2%
0.949 1
0.2%
0.919 1
0.2%
0.914 1
0.2%
0.887 1
0.2%
0.879 1
0.2%
0.821 1
0.2%
0.815 1
0.2%
0.786 2
0.4%

nokken_poole_dim2
Real number (ℝ)

Distinct435
Distinct (%)79.2%
Missing1
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean0.0041876138
Minimum-0.951
Maximum0.83
Zeros1
Zeros (%)0.2%
Negative264
Negative (%)48.0%
Memory size4.4 KiB
2023-08-28T14:37:40.091732image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-0.951
5-th percentile-0.544
Q1-0.177
median0.009
Q30.214
95-th percentile0.4746
Maximum0.83
Range1.781
Interquartile range (IQR)0.391

Descriptive statistics

Standard deviation0.30685629
Coefficient of variation (CV)73.277121
Kurtosis0.043555277
Mean0.0041876138
Median Absolute Deviation (MAD)0.194
Skewness-0.22859489
Sum2.299
Variance0.09416078
MonotonicityNot monotonic
2023-08-28T14:37:40.202875image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.132 5
 
0.9%
-0.097 4
 
0.7%
0.247 4
 
0.7%
0.168 4
 
0.7%
0.19 4
 
0.7%
0.355 3
 
0.5%
-0.309 3
 
0.5%
-0.179 3
 
0.5%
-0.132 3
 
0.5%
-0.166 3
 
0.5%
Other values (425) 513
93.3%
ValueCountFrequency (%)
-0.951 1
0.2%
-0.913 1
0.2%
-0.877 1
0.2%
-0.831 1
0.2%
-0.765 1
0.2%
-0.76 1
0.2%
-0.744 1
0.2%
-0.731 1
0.2%
-0.729 1
0.2%
-0.727 1
0.2%
ValueCountFrequency (%)
0.83 1
0.2%
0.773 1
0.2%
0.734 1
0.2%
0.703 1
0.2%
0.687 1
0.2%
0.659 1
0.2%
0.655 1
0.2%
0.646 1
0.2%
0.635 1
0.2%
0.625 1
0.2%

Interactions

2023-08-28T14:37:34.504486image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:23.929618image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:25.105887image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:26.125114image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:27.144046image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:28.162126image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:29.021163image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:30.236645image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:31.300005image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:32.281907image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:33.396678image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:34.592889image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:24.020811image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:25.191659image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:26.225033image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:27.240794image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:28.234261image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:29.110299image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:30.326732image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:31.388834image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:32.376103image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:33.512993image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:34.695702image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:24.109657image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:25.276921image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:26.318004image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:27.331470image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:28.318136image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:29.202976image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:30.424970image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:31.472907image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:32.466691image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:33.620893image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:34.787066image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:24.196146image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:25.362625image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:26.409040image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:27.427765image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:28.388330image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:29.291523image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:30.518985image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:31.557938image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:32.563447image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:33.714653image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:34.890606image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:24.457737image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:25.453037image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:26.497920image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:27.517704image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:28.471606image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:29.385744image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:30.621010image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:31.651565image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:32.655745image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:33.816023image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:34.967998image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:24.526270image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:25.533457image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:26.568495image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:27.594517image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:28.541687image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:29.465858image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:30.698452image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:31.727014image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:32.736725image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:33.892382image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:35.253202image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:24.619503image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:25.628525image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:26.663005image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:27.687645image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:28.621995image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:29.732306image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:30.804449image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:31.823208image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:32.848518image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:33.992595image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:35.353045image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:24.721214image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:25.724484image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:26.757107image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:27.783839image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:28.700869image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:29.842917image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:30.898693image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:31.914781image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:32.953244image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:34.098053image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:35.447033image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:24.814662image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:25.816208image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:26.841837image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:27.869572image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:28.769285image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:29.931545image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:30.986115image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:31.996553image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:33.043603image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:34.191221image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:35.550442image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:24.911398image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:25.910254image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:26.933715image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:27.965804image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:28.852766image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:30.038614image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:31.085626image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:32.086149image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:33.138705image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:34.295233image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:35.655456image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:25.013622image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:26.018425image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:27.045384image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:28.066642image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:28.934425image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:30.138153image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:31.196278image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:32.185899image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:33.261120image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:37:34.398369image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-08-28T14:37:40.313239image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
icpsrstate_icpsrdistrict_codeborndiednominate_dim1nominate_dim2nominate_log_likelihoodnominate_geo_mean_probabilitynokken_poole_dim1nokken_poole_dim2chamberstate_abbrevparty_codeoccupancylast_means
icpsr1.0000.014-0.1430.1370.142-0.0170.0460.2080.073-0.017-0.0010.5420.2690.0870.0670.311
state_icpsr0.0141.0000.091-0.0420.1830.1270.060-0.043-0.0480.1380.0650.7220.9610.2920.0000.579
district_code-0.1430.0911.0000.041-0.277-0.0700.042-0.329-0.090-0.0610.0400.2820.1510.0000.0000.000
born0.137-0.0420.0411.0000.3040.2620.076-0.112-0.0290.2500.0360.0600.0000.1290.0440.000
died0.1420.183-0.2770.3041.0000.1460.202-0.225-0.2110.0970.1530.0000.3040.0460.8991.000
nominate_dim1-0.0170.127-0.0700.2620.1461.0000.199-0.108-0.1820.9770.2090.1220.1990.7000.0680.070
nominate_dim20.0460.0600.0420.0760.2020.1991.0000.0610.0520.2030.8810.0000.2090.1970.0000.000
nominate_log_likelihood0.208-0.043-0.329-0.112-0.225-0.1080.0611.0000.801-0.1470.1190.3620.0700.1670.1630.159
nominate_geo_mean_probability0.073-0.048-0.090-0.029-0.211-0.1820.0520.8011.000-0.2200.0910.4110.0800.1970.0770.000
nokken_poole_dim1-0.0170.138-0.0610.2500.0970.9770.203-0.147-0.2201.0000.1990.1220.2770.8590.0000.000
nokken_poole_dim2-0.0010.0650.0400.0360.1530.2090.8810.1190.0910.1991.0000.1770.1950.2170.2220.117
chamber0.5420.7220.2820.0600.0000.1220.0000.3620.4110.1220.1771.0000.6970.0880.0520.718
state_abbrev0.2690.9610.1510.0000.3040.1990.2090.0700.0800.2770.1950.6971.0000.4440.0000.562
party_code0.0870.2920.0000.1290.0460.7000.1970.1670.1970.8590.2170.0880.4441.0000.0000.000
occupancy0.0670.0000.0000.0440.8990.0680.0000.1630.0770.0000.2220.0520.0000.0001.0000.539
last_means0.3110.5790.0000.0001.0000.0700.0000.1590.0000.0000.1170.7180.5620.0000.5391.000

Missing values

2023-08-28T14:37:35.824916image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-08-28T14:37:36.108617image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-08-28T14:37:36.287687image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

congresschambericpsrstate_icpsrdistrict_codestate_abbrevparty_codeoccupancylast_meansbionamebioguide_idborndiednominate_dim1nominate_dim2nominate_log_likelihoodnominate_geo_mean_probabilitynominate_number_of_votesnominate_number_of_errorsconditionalnokken_poole_dim1nokken_poole_dim2
0113President99911990USA10000OBAMA, BarackO0001671961NaN-0.358-0.197-41.044640.8820032711NaNNaNNaN
1113House20300411AL20011BONNER, Jr., Josiah Robins (Jo)B0012441959NaN0.3670.513-41.887180.8880035417NaN0.3310.625
2113House20301413AL20001ROGERS, Mike DennisR0005751958NaN0.3630.455-134.139920.87470100257NaN0.4510.659
3113House21102417AL10001SEWELL, TerriS0011851965NaN-0.3960.398-160.911630.85191100480NaN-0.4060.478
4113House21192412AL20001ROBY, MarthaR0005911976NaN0.3620.658-87.382060.91759101633NaN0.3160.734
5113House21193415AL20001BROOKS, MoB0012741954NaN0.652-0.417-207.051890.81481101176NaN0.576-0.225
6113House21376411AL20022BYRNE, BradleyB0012891955NaN0.6100.250-38.783130.9186445714NaN0.5710.368
7113House29301416AL20001BACHUS, Spencer T., IIIB0000131947NaN0.3870.228-134.439740.8740099666NaN0.3320.318
8113House29701414AL20001ADERHOLT, RobertA0000551965NaN0.3860.561-90.284700.9083393938NaN0.3560.773
9113House14066811AK20001YOUNG, Donald EdwinY00003319332022.00.2830.022-222.309870.7909694887NaN0.2650.116
congresschambericpsrstate_icpsrdistrict_codestate_abbrevparty_codeoccupancylast_meansbionamebioguide_idborndiednominate_dim1nominate_dim2nominate_log_likelihoodnominate_geo_mean_probabilitynominate_number_of_votesnominate_number_of_errorsconditionalnokken_poole_dim1nokken_poole_dim2
540113Senate40909400VA10001WARNER, MarkW0008051954NaN-0.205-0.051-43.749430.9221854016NaN-0.2090.051
541113Senate41305400VA10001KAINE, Timothy Michael (Tim)K0003841958NaN-0.245-0.062-28.452150.949135459NaN-0.245-0.041
542113Senate39310730WA10001CANTWELL, Maria E.C0001271958NaN-0.305-0.411-16.460950.970575515NaN-0.436-0.132
543113Senate49308730WA10001MURRAY, PattyM0011111950NaN-0.352-0.305-13.689220.974785363NaN-0.375-0.179
544113Senate14922560WV10001ROCKEFELLER, John Davison IV (Jay)R0003611937NaN-0.3260.177-21.952910.956004879NaN-0.3360.020
545113Senate40915560WV10001MANCHIN, Joe, IIIM0011831947NaN-0.0580.406-127.282090.7900154056NaN-0.0410.322
546113Senate29940250WI10001BALDWIN, TammyB0012301962NaN-0.492-0.135-18.575040.966795508NaN-0.457-0.244
547113Senate41111250WI20001JOHNSON, RonJ0002931955NaN0.629-0.146-91.881760.8424753632NaN0.685-0.348
548113Senate40707680WY20001BARRASSO, John A.B0012611952NaN0.5370.224-86.252860.8541254737NaN0.5620.284
549113Senate49706680WY20001ENZI, Michael B.E00028519442021.00.5450.199-81.508900.8617954831NaN0.5730.411